Methods and compositions for correlating genetic markers with prostate cancer risk

ABSTRACT

The present invention provides a method of identifying a subject as having an increased risk of developing prostate cancer, comprising detecting in the subject the presence of various polymorphisms associated with an increased risk of developing prostate cancer.

STATEMENT OF PRIORITY

This application is a continuation application of, and claims priority to, U.S. application Ser. No. 12/339,653, filed Dec. 19, 2008 (abandoned), which claims the benefit, under 35 U.S.C. § 119(e), of U.S. Provisional Application Ser. No. 61/016,117, filed Dec. 21, 2007, the entire contents of each of which are incorporated by reference herein.

GOVERNMENT SUPPORT

This invention was made with government support under grant # CA106523, CA95052, CA1125117, and CA58236 awarded by the National Institutes of Health and grant # PC051264 awarded by the Department of Defense. The government has certain rights in the invention.

STATEMENT REGARDING ELECTRONIC FILING OF A SEQUENCE LISTING

A Sequence Listing in ASCII text format, submitted under 37 C.F.R. § 1.821, entitled 9151-107TSCT ST25.txt, 10,019 bytes in size, generated on Jan. 5, 2018 and filed via EFS-Web, is provided in lieu of a paper copy. This Sequence Listing is hereby incorporated by reference herein into the specification for its disclosures.

FIELD OF THE INVENTION

The present invention provides methods and compositions directed to identification of genetic markers associated with prostate cancer.

BACKGROUND OF THE INVENTION

Genome-wide association (GWA) studies have identified sequence variants that are consistently associated with risk for complex diseases'. Such variants have limited utility in the assessment of disease risk in an individual, however, because most of them confer a relatively small risk. What is needed is a determination of whether combinations of individual variants confer larger, more clinically useful, increases in risk.

Age, race, and family history are the three risk factors that are consistently associated with the risk of prostate cancer³. A meta analysis found a pooled odds ratio of 2.5 for men who have an affected first-degree relative⁴. In the present invention, genetic variants in five chromosomal regions associated with a statistically significant risk of prostate cancer have been identified using genome-wide analysis. These include three independent regions at 8q24⁵⁻⁸ and one region each at 17q12 and 17q24.3⁹. While it is anticipated that these five regions harbor prostate cancer susceptibility genes or regulatory factors affecting critical genes, the specific genes in question have not been identified to date.

Thus, the present invention overcomes previous shortcomings in the art by identifying significant statistical associations between a combination of genetic markers in different chromosomal regions and prostate cancer. Thus, the present invention provides methods and compositions for identifying a subject at increased risk of developing prostate cancer by detecting the genetic markers of this invention.

SUMMARY OF THE INVENTION

In one aspect, the present invention provides a method of identifying a subject as having an increased risk of developing prostate cancer, comprising detecting in nucleic acid of the subject the presence of two or more polymorphisms associated with an increased risk of prostate cancer, wherein each of the two or more polymorphisms is present in a different chromosome region selected from the group consisting of:

a) chromosome region 17q12;

b) chromosome region 17q24.3;

c) chromosome region 8q24 (Region 2);

d) 8q24 (Region 3);

e) and 8q24 (Region 1); and

f) any combination of (a)-(e) above,

whereby the presence of said two or more polymorphisms identifies the subject as having an increased risk of developing prostate cancer. It is further provided that the methods of this invention comprise detecting three or more polymorphisms associated with an increased risk of prostate cancer, each from a different chromosome region among those listed as (a)-(e) above, in any combination; detecting four or more polymorphisms associated with an increased risk of prostate cancer, each from a different chromosome region among those listed as (a)-(e) above, in any combination; and/or detecting five polymorphisms associated with increased risk of prostate cancer, each from a different chromosome region among those listed as (a)-(e) above. The two, three, four or five polymorphisms can also be detected in combination with other polymorphisms associated with increased risk of prostate cancer, which can be present in the chromosome regions listed as (a)-(e) above (e.g., in linkage disequilibrium) and/or which can be present in other chromosome regions in which polymorphisms associated with increased prostate cancer risk are known or later identified to be present.

The methods of the present invention can also be employed in identifying a subject having an increased risk of developing prostate cancer by detecting the various polymorphisms and genetic markers described herein and further identifying a family history of prostate cancer in the subject, whereby the presence of any of the combinations of risk markers in the subject's genotypic makeup as described herein and a family history of prostate cancer identify the subject as having an increased risk of developing prostate cancer. The methods of this invention can also be used to supplement the predictive value of prostate serum antigen (PSA). Thus, a subject having any of the combinations of risk markers as described herein and an elevated and/or rising PSA serum level is a subject that has an increased risk of developing prostate cancer.

In a further aspect, the present invention provides a method of identifying a human subject as having an increased risk of developing prostate cancer, comprising detecting in the subject the presence of two or more alleles selected from the group consisting of:

a) the T allele of single nucleotide polymorphism rs4430796;

b) the G allele of single nucleotide polymorphism rs1859962;

c) the A allele of single nucleotide polymorphism rs16901979;

d) the G allele of single nucleotide polymorphism rs6983267;

e) the A allele of single nucleotide polymorphism rs1447295; and

f) any combination of (a), (b), (c) (d) and (e) above,

whereby the presence of said alleles identifies the subject as having an increased risk of developing prostate cancer. Thus, the methods of this invention can comprise detecting three or more alleles among those listed as (a)-(e) above, in any combination; detecting four or more alleles among those listed as (a)-(e) above, in any combination; and/or detecting all five of the alleles listed as (a)-(e) above. The two, three, four or five alleles can also be detected in combination with other alleles and/or polymorphisms, which can be present in any of the chromosome regions in which the alleles of (a)-(e) above are located (e.g., in linkage disequilibrium with any of the alleles of (a)-(e) above) and/or which can be present in other chromosome regions in which alleles associated with prostate cancer risk are known or later identified to be present.

DETAILED DESCRIPTION OF THE INVENTION

The present invention is explained in greater detail below. This description is not intended to be a detailed catalog of all the different ways in which the invention may be implemented, or all the features that may be added to the instant invention. For example, features illustrated with respect to one embodiment may be incorporated into other embodiments, and features illustrated with respect to a particular embodiment may be deleted from that embodiment. In addition, numerous variations and additions to the various embodiments suggested herein will be apparent to those skilled in the art in light of the instant disclosure, which do not depart from the instant invention. Hence, the following specification is intended to illustrate some particular embodiments of the invention, and not to exhaustively specify all permutations, combinations and variations thereof

The present invention is based on the unexpected discovery that the combination of alleles in various chromosome regions is statistically associated with an increased risk of developing prostate cancer. There are numerous benefits of carrying out the methods of this invention to identify a subject having an increased risk of developing prostate cancer, including but not limited to, identifying subjects who are good candidates for prophylactic and/or therapeutic treatment, and screening for cancer at an earlier time or more frequently than might otherwise be indicated, to increase the chances of early detection of a prostate cancer.

Thus, in one aspect, the present invention provides a method of identifying a subject (e.g., a human subject) as having an increased risk of developing prostate cancer, comprising detecting in nucleic acid of the subject the presence of two or more polymorphisms, wherein each of the two or more polymorphisms is present in a different chromosome region selected from the group consisting of:

a) chromosome region 17q12;

b) chromosome region 17q24.3;

c) chromosome region 8q24 (Region 2);

d) 8q24 (Region 3);

e) and 8q24 (Region 1); and

f) any combination of (a)-(e) above,

whereby the presence of said two or more polymorphisms identifies the subject as having an increased risk of developing prostate cancer.

As noted herein, the methods of this invention can comprise detecting three or more polymorphisms, each from a different chromosome region among those listed as (a)-(e) above, in any combination; detecting four or more polymorphisms, each from a different chromosome region among those listed as (a)-(e) above, in any combination; and/or detecting five polymorphisms, each from a different chromosome region among those listed as (a)-(e) above.

Thus, the present invention provides methods for detection of a polymorphism or genetic marker of this invention in any of the following combinations of chromosome regions, wherein a, b, c, d and e represent each chromosome region as listed herein.

Combinations of two alleles include: a and b; a and c; a and d; a and e; b and c; b and d; b and e; c and d; c and e; d and e.

Combinations of three alleles include: a, b and c; a, b and d; a, b and e; a, c and e; a, c and d; a, e and d; b, c and d; b, c and e; b, d and e; c, d and e.

Combinations of four alleles include: a, b, c and d; a, b, c and e; b, c, d and e; a, b, c and e; a, c, d and e; and a, b, d and e.

The two, three, four or five polymorphisms can also be detected in combination with other polymorphisms, present in any one two, three, four or five of the chromosome regions listed as (a)-(e) above and/or present in other chromosome regions in which polymorphisms and genetic markers associated with prostate cancer risk are known or later identified to be present.

In certain embodiments of this invention, the polymorphism in chromosome region 17q12 can be the T allele of the single nucleotide polymorphism having GenBank® database Accession No. rs4430796. In other embodiments, the polymorphism in chromosome region 17q24.3 can be the G allele of the single nucleotide polymorphism having GenBank® database Accession No. rs1859962. In further embodiments, the polymorphism in chromosome region 8q24 (Region 1) can be the A allele of the single nucleotide polymorphism having GenBank® database Accession No. rs1447295. In still further embodiments, the polymorphism in chromosome region 8q24 (Region 2) can be the A allele of the single nucleotide polymorphism having GenBank® database Accession No. rs16901979. In other embodiments, the polymorphism in chromosome region 8124 (Region 3) can be the G allele of the single nucleotide polymorphism having GenBank® database Accession No. rs6983267.

In a further aspect, the present invention provides a method of identifying a human subject as having an increased risk of developing prostate cancer, comprising detecting in the subject the presence of two or more alleles selected from the group consisting of:

a) the T allele of the single nucleotide polymorphism having GenBank® database Accession No. rs4430796;

b) the G allele of the single nucleotide polymorphism having GenBank® database Accession No. rs1859962;

c) the A allele of the single nucleotide polymorphism having GenBank® database Accession No. rs16901979;

d) the G allele of the single nucleotide polymorphism having GenBank® database Accession No. rs6983267;

e) the A allele of the single nucleotide polymorphism having GenBank® database Accession No. rs1447295; and

f) any combination of (a), (b), (c) (d) and (e) above,

whereby the presence of said alleles identifies the subject as having an increased risk of developing prostate cancer. Thus, the methods of this invention can further comprise detecting, in a subject, three or more alleles among those listed as (a)-(e) above, in any combination; detecting four or more alleles among those listed as (a)-(e) above, in any combination; and/or detecting all five of the alleles listed as (a)-(e) above. The two, three, four or five alleles can also be detected in combination with other alleles, which can be present in the chromosome regions in which the alleles of (a)-(e) above are located and/or which can be present in other chromosome regions in which alleles associated with prostate cancer risk are known or later identified to be present.

Thus, for example, the following combinations of alleles can be detected according to the methods of this invention to identify a subject as having an increased risk of developing prostate cancer, wherein a, b, c, d and e represent each of the alleles as listed herein.

Combinations of two alleles can include: a and b; a and c; a and d; a and e; b and c; b and d; b and e; c and d; c and e; d and e.

Combinations of three alleles can include: a, b and c; a, b and d; a, b and e; a, can d e; a, c and d; a, e and d; b, c and d; b, c and e; b, d and e; c, d and e.

Combinations of four alleles include: a, b, c and d; a, b, c and e; b, c, d and e; a, b, c and e; a, c, d and e; and a, b, d and e.

Additional risk alleles that can be detected in the methods of this invention to identify a subject as having an increased risk of developing prostate cancer, with and without a family history of prostate cancer and/or with and without an elevated and/or rising PSA level are described in Tables 8-12 herein. These alleles can be present in any combination with any of the five alleles described above as (a)-(e) and/or in any combination with one another.

The present invention further provides embodiments wherein a subject of this invention is heterozygous for an allele of this invention and other embodiments wherein a subject of this invention is homozygous for an allele of this invention. In the methods provided herein wherein a combination of alleles is analyzed, the subject can be heterozygous or homozygous for any given allele in any combination relative to the other alleles in the combination.

In certain embodiments of this invention, the methods described herein can be employed to identify 1) a subject at increased or decreased risk of a more aggressive form of prostate cancer (e.g., having a Gleason score of 7 (4+3) to 10), 2) a subject at increased or decreased risk of a poor prognosis (e.g., increased likelihood the cancer will metastasize, will be poorly responsive to treatment and/or will lead to death) once cancer has been diagnosed in the subject; and/or 3) a subject at increased or decreased risk of an early age of onset of prostate cancer, by identifying in the subject the polymorphisms and/or alleles of this invention.

It is further contemplated that the methods of this invention can be carried out to diagnose prostate cancer in a subject, by detecting the combinations of polymorphisms or genetic markers described herein.

In further aspects, the present invention provides a kit for carrying out the methods of this invention, wherein the kit can comprise primers, probes, primer/probe sets, reagents, buffers, etc., as would be known in the art, for the detection of the polymorphisms and/or alleles of this invention in a nucleic acid sample from a subject. For example, a primer or probe can comprise a contiguous nucleotide sequence that is complementary to a region comprising a polymorphism or genetic marker of this invention. In particular embodiments, a kit of this invention will comprise primers and probes that allow for the specific detection of the polymorphisms and genetic markers of this invention. Such a kit can further comprise blocking probes, labeling reagents, blocking agents, restriction enzymes, antibodies, sampling devices, positive and negative controls, etc., as would be well known to those of ordinary skill in the art.

Definitions

As used herein, “a,” “an” or “the” can mean one or more than one. For example, “a” cell can mean a single cell or a multiplicity of cells.

Also as used herein, “and/or” refers to and encompasses any and all possible combinations of one or more of the associated listed items, as well as the lack of combinations when interpreted in the alternative (“or”).

Furthermore, the term “about,” as used herein when referring to a measurable value such as an amount of a compound or agent of this invention, dose, time, temperature, and the like, is meant to encompass variations of ±20%, ±10%, ±5%, ±1%, ±0.5%, or even ±0.1% of the specified amount.

As used herein, the term “prostate cancer” describes an uncontrolled (malignant) growth of cells in the prostate gland, which is located at the base of the urinary bladder and is responsible for helping control urination as well as forming part of the semen. Symptoms of prostate cancer can include, but are not limited to, urinary problems (e.g., not being able to urinate; having a hard time starting or stopping the urine flow; needing to urinate often, especially at night; weak flow of urine; urine flow that starts and stops; pain or burning during urination), difficulty having an erection, blood in the urine or semen, and/or frequent pain in the lower back, hips, or upper thighs.

The term “chromosome region” as used herein refers to a part of a chromosome defined either by anatomical details, especially by banding, or by its linkage groups. The particular chromosome regions of this invention are further defined by the following boundaries.

Chromosome region 17q12: Region around rs4430796 (chr17:33,172,153): from 33,163,028 to 33,189,279, ˜20 Kb, #SNPs=11 (Table 8).

Chromosome region 17q24.2: Region around rs1859962 (chr17:66,20,348): from 66,616,533 to 66,754,527, ˜140 Kb, #SNPs=174 (Table 9).

Chromosome region 8q24 (Region 2): Region around rs16901979 (chr8:128,194,098): from 128,145,397 to 128,215,780), ˜70 kb, # SNPs=112 (Table 11).

Chromosome region 8q24 (Region 3): Region around rs6983267 (chr8:128,482,487): from 128,469,358 to 128,535,996, ˜65 kb, #SNPs=70 (Table 12).

Chromosome region 8q24 (Region 1): Region around rs1447295 (chr8:128,554,220): from 128,536,936 to 128,617,860, ˜80 kb, #SNPs=116 (Table 10).

All the positions described above are based on Build 35 and the SNPs are based on Hapmap SNP release 21.

Also as used herein, “linked” describes a region of a chromosome that is shared more frequently in family members or members of a population manifesting a particular phenotype and/or affected by a particular disease or disorder, than would be expected or observed by chance, thereby indicating that the gene or genes or other identified marker(s) within the linked chromosome region contain or are associated with an allele that is correlated with the phenotype and/or presence of a disease or disorder, or with an increased or decreased likelihood of the phenotype and/or of the disease or disorder. Once linkage is established, association studies (linkage disequilibrium) can be used to narrow the region of interest or to identify the marker (e.g., allele or haplotype) correlated with the phenotype and/or disease or disorder.

Furthermore, as used herein, the term “linkage disequilibrium” or “LD” refers to the occurrence in a population of two linked alleles at a frequency higher or lower than expected on the basis of the gene frequencies of the individual genes. Thus, linkage disequilibrium describes a situation where alleles occur together more often than can be accounted for by chance, which indicates that the two alleles are physically close on a DNA strand.

The term “genetic marker” or “polymorphism” as used herein refers to a characteristic of a nucleotide sequence (e.g., in a chromosome) that is identifiable due to its variability among different subjects (i.e., the genetic marker or polymorphism can be a single nucleotide polymorphism, a restriction fragment length polymorphism, a microsatellite, a deletion of nucleotides, an addition of nucleotides, a substitution of nucleotides, a repeat or duplication of nucleotides, a translocation of nucleotides, and/or an aberrant or alternate splice site resulting in production of a truncated or extended form of a protein, etc., as would be well known to one of ordinary skill in the art).

A “single nucleotide polymorphism” (SNP) in a nucleotide sequence is a genetic marker that is polymorphic for two (or in some case three or four) alleles. SNPs can be present within a coding sequence of a gene, within noncoding regions of a gene and/or in an intergenic (e.g., intron) region of a gene. A SNP in a coding region in which both forms lead to the same polypeptide sequence is termed synonymous (i.e., a silent mutation) and if a different polypeptide sequence is produced, the alleles of that SNP are non-synonymous. SNPs that are not in protein coding regions can still have effects on gene splicing, transcription factor binding and/or the sequence of non-coding RNA.

The SNP nomenclature provided herein refers to the official Reference SNP (rs) identification number as assigned to each unique SNP by the National Center for Biotechnological Information (NCBI), which is available in the GenBank® database.

In some embodiments, the term genetic marker is also intended to describe a phenotypic effect of an allele or haplotype, including for example, an increased or decreased amount of a messenger RNA, an increased or decreased amount of protein, an increase or decrease in the copy number of a gene, production of a defective protein, tissue or organ, etc., as would be well known to one of ordinary skill in the art.

An “allele” as used herein refers to one of two or more alternative forms of a nucleotide sequence at a given position (locus) on a chromosome. Usually alleles are nucleotides present in a nucleotide sequence that makes up the coding sequence of a gene, but sometimes the term is used to refer to a nucleotide in a non-coding region of a gene. An individual's genotype for a given gene is the set of alleles it happens to possess. As noted herein, an individual can be heterozygous or homozygous for an allele of this invention.

Also as used herein, a “haplotype” is a set of SNPs on a single chromatid that are statistically associated. It is thought that these associations, and the identification of a few alleles of a haplotype block, can unambiguously identify all other polymorphic sites in its region. The term “haplotype” is also commonly used to describe the genetic constitution of individuals with respect to one member of a pair of allelic genes; sets of single alleles or closely linked genes that tend to be inherited together.

The terms “increased risk” and “decreased risk” as used herein define the level of risk that a subject has of developing prostate cancer, as compared to a control subject that does not have the polymorphisms and genetic markers of this invention in the control subject's nucleic acid.

A sample of this invention can be any sample containing nucleic acid of a subject, as would be well known to one of ordinary skill in the art. Nonlimiting examples of a sample of this invention include a cell, a body fluid, a tissue, a washing, a swabbing, etc., as would be well known in the art.

A subject of this invention is any animal that is susceptible to prostate cancer as defined herein and can include, for example, humans, as well as animal models of prostate cancer (e.g., rats, mice, dogs, nonhuman primates, etc.). In some aspects of this invention, the subject can be a Caucasian (e.g., white; European-American; Hispanic) human and in other aspects the subject can be a human of black African ancestry (e.g., black; African American; African-European; African-Caribbean, etc.). In yet other aspects the subject can be Asian. In further aspects of this invention, the subject has a family history of prostate cancer (e.g., having at least one first degree relative diagnosed with prostate cancer) and in some embodiments, the subject does not have a family history of prostate cancer. Additionally a subject of this invention has a diagnosis of prostate cancer in certain embodiments and in other embodiments, a subject of this invention does not have a diagnosis of prostate cancer.

As used herein, “nucleic acid” encompasses both RNA and DNA, including cDNA, genomic DNA, mRNA, synthetic (e.g., chemically synthesized) DNA and chimeras, fusions and/or hybrids of RNA and DNA. The nucleic acid can be double-stranded or single-stranded. Where single-stranded, the nucleic acid can be a sense strand or an antisense strand. The nucleic acid can be synthesized using oligonucleotide analogs or derivatives (e.g., inosine or phosphorothioate nucleotides, etc.). Such oligonucleotides can be used, for example, to prepare nucleic acids that have altered base-pairing abilities or increased resistance to nucleases.

An “isolated nucleic acid” is a nucleotide sequence that is not immediately contiguous with nucleotide sequences with which it is immediately contiguous (one on the 5′ end and one on the 3′ end) in the naturally occurring genome of the organism from which it is derived. Thus, in one embodiment, an isolated nucleic acid includes some or all of the 5′ non-coding (e.g., promoter) sequences that are immediately contiguous to a coding sequence. The term therefore includes, for example, a recombinant DNA that is incorporated into a vector, into an autonomously replicating plasmid or virus, or into the genomic DNA of a prokaryote or eukaryote, or which exists as a separate molecule (e.g., a cDNA or a genomic DNA fragment produced by PCR or restriction endonuclease treatment), independent of other sequences. It also includes a recombinant DNA that is part of a hybrid nucleic acid encoding an additional polypeptide or peptide sequence.

The term “isolated” can refer to a nucleic acid or polypeptide that is substantially free of cellular material, viral material, and/or culture medium (e.g., when produced by recombinant DNA techniques), or chemical precursors or other chemicals (when chemically synthesized). Moreover, an “isolated fragment” is a fragment of a nucleic acid or polypeptide that is not naturally occurring as a fragment and would not be found in the natural state.

The term “oligonucleotide” refers to a nucleic acid sequence of at least about six nucleotides to about 100 nucleotides, for example, about 15 to about 30 nucleotides, or about 20 to about 25 nucleotides, which can be used, for example, as a primer in a PCR amplification and/or as a probe in a hybridization assay or in a microarray. Oligonucleotides of this invention can be natural or synthetic, e.g., DNA, RNA, PNA, LNA, modified backbones, etc., as are well known in the art.

The present invention further provides fragments of the nucleic acids of this invention, which can be used, for example, as primers and/or probes. Such fragments or oligonucleotides can be detectably labeled or modified, for example, to include and/or incorporate a restriction enzyme cleavage site when employed as a primer in an amplification (e.g., PCR) assay.

The detection of a polymorphism, genetic marker or allele of this invention can be carried out according to various protocols standard in the art and as described herein for analyzing nucleic acid samples and nucleotide sequences, as well as identifying specific nucleotides in a nucleotide sequence.

For example, nucleic acid can be obtained from any suitable sample from the subject that will contain nucleic acid and the nucleic acid can then be prepared and analyzed according to well-established protocols for the presence of genetic markers according to the methods of this invention. In some embodiments, analysis of the nucleic acid can be carried by amplification of the region of interest according to amplification protocols well known in the art (e.g., polymerase chain reaction, ligase chain reaction, strand displacement amplification, transcription-based amplification, self-sustained sequence replication (3SR), Qβ replicase protocols, nucleic acid sequence-based amplification (NASBA), repair chain reaction (RCR) and boomerang DNA amplification (BDA), etc.). The amplification product can then be visualized directly in a gel by staining or the product can be detected by hybridization with a detectable probe. When amplification conditions allow for amplification of all allelic types of a genetic marker, the types can be distinguished by a variety of well-known methods, such as hybridization with an allele-specific probe, secondary amplification with allele-specific primers, by restriction endonuclease digestion, and/or by electrophoresis. Thus, the present invention further provides oligonucleotides for use as primers and/or probes for detecting and/or identifying genetic markers according to the methods of this invention.

The genetic markers of this invention are correlated with (i.e., identified to be statistically associated with) prostate cancer as described herein according to methods well known in the art and as disclosed in the Examples provided herein for statistically correlating genetic markers with various phenotypic traits, including disease states and pathological conditions as well as determining levels of risk associated with developing a particular phenotype, such as a disease or pathological condition. In general, identifying such correlation involves conducting analyses that establish a statistically significant association and/or a statistically significant correlation between the presence of a genetic marker or a combination of markers and the phenotypic trait in a population of subjects and controls (e.g., ethnically matched controls). The correlation can involve one or more than one genetic marker of this invention (e.g., two, three, four, five, or more) in any combination. An analysis that identifies a statistical association (e.g., a significant association) between the marker or combination of markers and the phenotype establishes a correlation between the presence of the marker or combination of markers in a population of subjects and the particular phenotype being analyzed. A level of risk (e.g., increased or decreased) can then be determined for an individual on the basis of such population-based analyses.

Thus, in certain embodiments, the present invention provides a method of screening a subject for polymorphisms that are associated with prostate cancer, comprising: a) performing a population based study to detect polymorphisms in a group of subjects with prostate cancer and ethnically matched controls; b) identifying polymorphisms in the group of subjects that are statistically associated with prostate cancer; and c) screening a subject for the presence of the polymorphisms identified in step (b).

The present invention further provides a method of identifying an effective and/or appropriate (i.e., for a given subject's particular condition or status) treatment regimen for a subject with prostate cancer, comprising detecting one or more of the polymorphisms and genetic markers associated with prostate cancer of this invention in the subject, wherein the one or more polymorphisms and genetic markers are further statistically correlated with an effective and/or appropriate treatment regimen for prostate cancer according to protocols as described herein and as are well known in the art.

Also provided is a method of identifying an effective and/or appropriate treatment regimen for a subject with prostate cancer, comprising: a) correlating the presence of one or more genetic markers of this invention in a test subject or population of test subjects with prostate cancer for whom an effective and/or appropriate treatment regimen has been identified; and b) detecting the one or more markers of step (a) in the subject, thereby identifying an effective and/or appropriate treatment regimen for the subject.

Further provided is a method of correlating a polymorphism or genetic marker of this invention with an effective and/or appropriate treatment regimen for prostate cancer, comprising: a) detecting in a subject or a population of subjects with prostate cancer and for whom an effective and/or appropriate treatment regimen has been identified, the presence of one or more genetic markers or polymorphisms of this invention; and b) correlating the presence of the one or more genetic markers of step (a) with an effective treatment regimen for prostate cancer.

Examples of treatment regimens for prostate cancer are well known in the art. Subjects who respond well to particular treatment protocols can be analyzed for specific genetic markers and a correlation can be established according to the methods provided herein. Alternatively, subjects who respond poorly to a particular treatment regimen can also be analyzed for particular genetic markers correlated with the poor response. Then, a subject who is a candidate for treatment for prostate cancer can be assessed for the presence of the appropriate genetic markers and the most effective and/or appropriate treatment regimen can be provided.

In some embodiments, the methods of correlating genetic markers with treatment regimens of this invention can be carried out using a computer database. Thus the present invention provides a computer-assisted method of identifying a proposed treatment for prostate cancer. The method involves the steps of (a) storing a database of biological data for a plurality of subjects, the biological data that is being stored including for each of said plurality of subjects, for example, (i) a treatment type, (ii) at least one genetic marker associated with prostate cancer and (iii) at least one disease progression measure for prostate cancer from which treatment efficacy can be determined; and then (b) querying the database to determine the dependence on said genetic marker of the effectiveness of a treatment type in treating prostate cancer, to thereby identify a proposed treatment as an effective and/or appropriate treatment for a subject carrying a genetic marker correlated with prostate cancer.

In one embodiment, treatment information for a subject is entered into the database (through any suitable means such as a window or text interface), genetic marker information for that subject is entered into the database, and disease progression information is entered into the database. These steps are then repeated until the desired number of subjects has been entered into the database. The database can then be queried to determine whether a particular treatment is effective for subjects carrying a particular marker or combination of markers, not effective for subjects carrying a particular marker or combination of markers, etc. Such querying can be carried out prospectively or retrospectively on the database by any suitable means, but is generally done by statistical analysis in accordance with known techniques, as described herein.

The present invention is more particularly described in the following examples that are intended as illustrative only since numerous modifications and variations therein will be apparent to those skilled in the art.

EXAMPLES Example 1 Cumulative Effect of SNPs in the Five Chromosomal Regions of this Invention on Prostate Cancer Risk in a Caucasian Population Study Sample

The study sample was described in detail elsewhere¹⁰. Briefly, a large-scale population-based case-control study was conducted in Sweden, named CAPS (CAncer Prostate in Sweden). Prostate cancer patients were identified and recruited from four of the six regional cancer registries in Sweden. The inclusion criterion for case subjects was pathological or cytological verified adenocarcinoma of the prostate, diagnosed between July, 2001 and October, 2003. Among 3,648 identified prostate cancer case subjects, 3,161 (87%) agreed to participate. DNA samples from blood and TNM stage, Gleason grade (biopsy), and PSA levels at diagnosis were available for 2,893 patients (91%). These case subjects were classified as having advanced disease if they met any of the following criteria: T3/4, N+, M+, Gleason score sum ≥8, or PSA >50 ng/ml; otherwise, they were classified as localized. Control subjects were recruited concurrently with case subjects. They were randomly selected from the Swedish Population Registry, and matched according to the expected age distribution of cases (groups of five-year intervals) and geographical region. A total of 3,153 controls were invited and 2,149 (68%) agreed to participate. DNA samples from blood were available for 1,781 control subjects (83%). Serum PSA level was measured for all control subjects but was not used as an exclusion variable. A history of prostate cancer among first-degree relatives was obtained from a questionnaire for both cases and controls. Table 1 presents the demographic and clinical characteristics of the study subjects, which were Caucasian. Recruitment of the study population was completed in two phases, each with a similar number of subjects, those before October 31, 2002 (CAPS1) and after Nov. 1, 2002 (CAPS2). Each participant gave written informed consent. The study received institutional approval at the Karolinska Institute, Umea University, and Wake Forest University School of Medicine.

Selection of SNPs and SNP Genotyping

Sixteen SNPs from five chromosomal regions (three at 8q24 and one each at 17q12 and 17q24.3) that have been reported to be associated with prostate cancer^(7-9,11) were selected for this study. Polymerase chain reaction (PCR) and extension primers for these SNPs were designed using the MassARRAY Assay Design 3.0 software (Sequenom, Inc). The primer information is shown in Table 13. PCR and extension reactions were performed according to the manufacturer's instructions, and extension product sizes were determined by mass spectrometry using the Sequenom iPLEX system. Duplicate test samples and two water samples (PCR negative controls) that were blinded to the technician were included in each 96-well plate. The rate of concordant results between duplicate samples was >99%.

Statistical Analyses

Tests for Hardy-Weinberg equilibrium were performed for each SNP separately among case patients and control subjects using Fisher's exact test. Pair-wise linkage disequilibrium (LD) was tested for SNPs within each of the five chromosomal regions in control subjects using SAS/Genetics software (Version 9.0).

Allele frequency differences between case patients and control subjects were tested for each SNP using a chi-square test with 1 degree of freedom. Allelic odds ratio (OR) and 95% confidence interval (95% CI) were estimated based on a multiplicative model. For genotypes, a series of tests assuming an additive, dominant, or recessive genetic model were performed for each of the five SNPs using unconditional logistic regression with adjustment for age and geographic region, and the model that had the highest likelihood was considered as the best-fitting genetic model for the respective SNP.

The independent effect of each of the five previously implicated regions was tested by including the most significant SNP from each of the five regions in a logistic regression model using a backward selection procedure. Multiplicative interactions between SNPs were tested for each pair of SNPs by including both main effects and an interaction term (product of two main effects) in a logistic regression model. The cumulative effects of the five SNPs on prostate cancer were tested by counting the number of prostate cancer associated genotypes (based on the best-fitting genetic model from single SNP analysis) for these five SNPs in each subject. The OR for prostate cancer for men carrying any combination of 1, 2, 3, or ≥4 prostate cancer associated genotypes was estimated by comparing to men carrying none of the prostate cancer associated genotypes using logistic regression analysis. Tests were also performed for cumulative effect on prostate cancer association, which included five SNPs and family history.

Population attributable risk (PAR) was estimated for SNPs that remained significant after adjusting other covariates using the formula PAR=100%×p(OR −1)/[p(OR−1)+1], where p is the prevalence of prostate cancer associated genotypes among control subjects¹². The joint PAR was calculated as

$1 - \left( {\prod\limits_{i = 1}^{5}\; \left( {1 - {PAR}_{i}} \right)} \right)$

where PARi is the individual PAR for each associated SNP calculated under the full model and assuming no multiplicative interaction between the SNPs.

Associations of these five SNPs with TNM stages, aggressiveness of prostate cancer (advanced or localized prostate cancer), and family history (yes or no) were tested among cases only using a chi-square test of 2×N table. A trend test was used to assess the proportion of prostate cancer associated genotypes with each increasing Gleason score, from ≤4 to 10. Associations of SNPs with mean age at diagnosis were tested among cases only using a two sample t-test. Because serum PSA levels were not normally distributed, a non-parametric analysis (Wilcoxon rank sum test) was used to assess association between SNPs and pre-operative serum PSA level in cases or PSA levels at the time of sampling in controls. All reported P-values were based on a two sided test.

Results

Sixteen SNPs in five chromosomal regions (three at 8q24 and two on 17q), which were previously implicated in harboring putative genes related to susceptibility to prostate cancer were evaluated. In the control group, each SNP was in Hardy-Weinberg equilibrium (P≥0.05). Significant pair-wise linkage disequilibrium (P<0.05) was observed for the SNPs within each region.

Table 2 lists allele frequencies of the 16 SNPs among case and control subjects and shows the results of allelic and genotypic tests. Significantly different frequencies (P<0.05) between case and control subjects were observed for SNPs in each of the five chromosomal regions. At 17q12, SNP rs4430796 had the strongest association with prostate cancer; the frequency of allele ‘T’ (SNP rs4430796) was 0.61 in cases and 0.56 in controls (P=6.0×10⁻⁷). Of the four SNPs at 17q24.3, three were associated with prostate cancer, but only rs1859962 had a highly statistically significant association (P=2.1×10⁻⁴). The results for 17q12 and 17q24.3 were similar to those of a previous report⁹. For SNPs at 8q24, statistically significant associations with prostate cancer were found for all SNPs examined across the three independent regions at 8q24. Of the 16 SNPs, 13 remained significant at P<0.05 after adjusting for 16 tests using a Bonferroni correction.

Similar to the results of allelic tests, carriers of previously reported risk associated alleles for SNPs at 17q12, 17q24.3, and 8q24 were significantly more likely to be prostate cancer cases (Table 2). When various genetic models were tested for SNPs at each region, a recessive model was the best-fitting genetic model for SNPs at 17q12 and 17q24.3, and a dominant model was the best-fitting genetic model for SNPs at Regions 1, 2, and 3 of 8q24.

Due to strong genetic dependence (linkage disequilibrium) among SNPs within each region, for a combined analysis, it was possible to select one SNP (the most significant SNP from single SNP analysis) to represent each of the five regions in tests for their independent association with prostate cancer (Table 3). When these five SNPs were included in a multivariate logistic regression model, each of the five SNPs remained significantly associated with prostate cancer after adjusting for other SNPs, and each continued to be highly significant when family history was included in the model. The population attributable risks, based on adjusted ORs, for each of these five SNPs and positive family history were estimated to account for 4% to 21% of prostate cancer in this Swedish study population. The estimated joint population attributable risk for prostate cancer of the five associated SNPs plus family history was 46% in the Swedish population studied.

When multiplicative interaction was tested for each possible pair of these five SNPs using an interaction term in logistic regression, none was significant at P <0.05. However, these five SNPs appeared to have a cumulative effect on the association with prostate cancer diagnosis, adjusting for age, geographic region, and family history (Table 4). When compared with men who did not carry any prostate cancer associated genotype of these five SNPs, men that carried any combination of 1, 2, 3, or ≥4 prostate cancer associated genotypes had increasingly higher likelihood to be a prostate cancer case (P-trend=3.33×10¹⁸). When family history was included as another risk factor (coded as 0 or 1) for a total of 6 possible prostate cancer associated factors, a stronger cumulative effect on prostate cancer association was observed, adjusting for age and geographic region (P-trend=3.93×10⁻²⁸). For example, compared with men who carried none of the six prostate cancer associated factors, men that carried any five or more of these associated factors had an OR of 9.48 (95% CI: 3.65-24.64, P=8.94×10⁻⁹) for prostate cancer. This cumulative effect was similarly observed in two subsets of CAPS study subjects, P-trend=1.36×10⁻¹⁰ for CAPS1 and P-trend=9.03×10⁻²⁰ for CAPS2

The specificity and sensitivity of the regression model was calculated by constructing receiver operating characteristic (ROC) curves and calculated the area under the curve (AUC) statistics to estimate each model's ability to discriminate cases from control subjects. The AUC was 57.7 (95% CI: 56.0-59.3), 60.8 (59.1-62.4), and 63.3 (61.7-65.0), respectively, for the model with (1) age and region alone, (2) age, region and family history, and (3) age, region, family history and number of prostate cancer associated genotypes at the five SNPs. The AUC was significantly higher for model (3) than for model (2), P=6.12×10⁻⁶. It is important to note that these results may suffer from model over-fitting.

Table 5 shows that none of the five SNPs was significantly associated with aggressiveness of prostate cancer, Gleason score, family history, serum PSA level at diagnosis, or age at diagnosis (P>0.05). Furthermore, no associations with these clinical variables were found when multiple prostate cancer associated SNPs were considered simultaneously. For example, the 154 cases that carried four or more prostate cancer associated genotypes of these five SNPs were not significantly different from 162 cases that did not carry any prostate cancer associated genotype in terms of these clinical variables; positive family history was 17% and 21%, respectively (P=0.39), the proportion of advanced cases was 54% and 48%, respectively (P =0.33), and median serum PSA levels at diagnosis were 15 ng/ml and 14 ng/ml, respectively (P=0.27). A lack of association between these SNPs at 8q24 and clinical characteristics was also observed in previous studies8,13,14,16, while in other studies a trend of 8q24 prostate cancer associated alleles has been reported as occurring more often in patients with higher Gleason grade, stage or aggressive disease^(5-7,15,17).

Multiple chromosomal regions at 8q24 and 17q have been reported to be associated, at genome-wide significance level, with prostate cancer.⁵⁻⁹ While all three regions at 8q24 have been replicated in all published studies,^(11,13-17) no replication result has been published for regions at 17q. The highly statistically significant findings at 17q12 and 17q24.3 in this study provide the first independent confirmation for these two regions at 17q. In addition, the association of SNPs at Regions 1, 2, and 3 of 8q24 with prostate cancer was also confirmed. The discovery and confirmation of these five chromosomal regions that are associated with prostate cancer supports the value and potential of genetic association studies in complex diseases.

Although each of the SNPs in the five chromosomal regions was moderately associated with prostate cancer, the present study reveals that they have a stronger cumulative effect on prostate cancer association. It was estimated that men having 5 or more of the prostate cancer associated factors (prostate cancer associated genotypes at five SNPs and a positive family history of prostate cancer) have an odds ratio of 9.48 for prostate cancer. The cumulative effect is highly significant in the overall CAPS sample (P-trend=3.93×10⁻²⁸) and consistent between the two subsets of CAPS study subjects, P-trend=1.36×10⁻¹⁰ for CAPS1 and P-trend=9.03×10⁻²⁰ for CAPS2. Thus, the combined information from the five SNPs and family history can be used according to the present invention to assess an individual's risk of prostate cancer.

It was found that the presence of the five prostate cancer associated SNPs was independent of PSA levels in cases (Table 5) and controls, which suggests that some men with low PSA levels may have an increased risk of prostate cancer if they carry one or more prostate cancer associated genotypes described here. Studies using prediagnostic PSA in combination with the associated SNPs and family history will provide further insight into this aspect of the present invention.

The mechanism by which the SNPs analyzed in this study could affect the risk of prostate cancer has not been elucidated. Other than SNP rs4430796, which is located within the TCF2 gene, the specific genes affected by the rest of the SNPs have not been identified. As the five SNPs in this study appear to be associated with risk of prostate cancer in general, rather than with a more or less aggressive form, it is possible that the genetic variants described herein act at an early stage of carcinogenesis.

Example 2 Cumulative Effect of SNPs in the Five Chromosome Regions of this Invention on Prostate Cancer Risk in an African American Population Study Population

The African American study population cases consisted of 373 prostate cancer patients undergoing treatment for prostate cancer in the Department of Urology at Johns Hopkins Hospital from 1999 to 2006. The average age at diagnosis was 57 years (median, 56 years), and the range was 36-74 years. The 372 control individuals were men undergoing disease screening and were not thought to have prostate cancer on the basis of a physical exam and a serum prostate-specific antigen (PSA) value below 4 ng/ml. Both cases and controls were self-reported African Americans (i.e., of black African ancestry). The Institutional Review Board of Johns Hopkins University approved the study protocol.

Statistical Methods

Similar statistical methods as described in Example 1 are used to assess the cumulative effect of the SNPs of this invention in the five chromosome regions described herein on prostate cancer risk in African Americans.

Results

As shown in Table 6, the risk of developing prostate cancer in African Americans increases as the number of risk genotypes of the five variants of this invention increased, in the same manner as shown for the Caucasian population described in Example 1.

Example 3 Stronger Cumulative Effect of the Five Risk Variants and Family History on Early Age of Onset Prostate Cancer Study Population

The study population is the same Swedish population described in Example 1.

Statistical methods

Similar statistical methods as described in Example 1 are used to assess the cumulative effect of the SNPs of this invention in the five chromosome regions described herein and family history on early age of onset of prostate cancer. Age-specific odds ratios were calculated in three intervals (<65, 65-69, >69).

Results

As shown in Table 7, ORs for prostate cancer are stronger in prostate cancer subjects with early age of onset (<65 years) than in the other groups. For example, OR was 25.94 for men with ≥5 risk factors (five risk variants and family history) among men <65 years, compared with OR of 8.27 and 4.51 among men at age 65-69 years and at age >69, respectively.

The foregoing is illustrative of the present invention, and is not to be construed as limiting thereof. The invention is defined by the following claims, with equivalents of the claims to be included therein.

All publications, patent applications, patents, patent publications, all sequences identified by GenBank® database and/or SNP accession numbers, and other references cited herein are incorporated by reference in their entireties for the sequences and/or teachings relevant to the sentence and/or paragraph and/or claim in which the reference is presented.

REFERENCES

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TABLE 1 Clinical and demographic characteristics of study subjects # (%) of cases # (%) of Aggressive Localized All cases controls Characteristics (N = 1,231 ) (N = 1,619) (N = 2,893) (N = 1,728) Age at enrollment (Year) Mean (sd) 68.04 (7.32)   65.14 (6.74)   66.36 (7.13)   67.15 (7.39)  Family History (first-degree relatives) No 1013 (82.29)  1295 (79.99)  2342 (80.95)  1565 (90.57) Yes 218 (17.71) 324 (20.01) 551 (19.05) 163 (9.43) PSA levels at diagnosis for cases or at enrollment for controls (ng/ml) ≤4 36 (2.95) 185 (11.61) 221 (7.85)  1439 (83.32) 5-9.99 171 (14.00) 755 (47.39) 926 (32.91) 233 (13.5) 10-19.99 216 (17.69) 438 (27.50) 654 (23.24)  38 (2.20) 20-49.99 252 (20.64) 215 (13.50) 467 (16.60)  14 (0.81) 50-99.99 229 (18.76) 0 229 (8.14)   2 (0.09) ≥100  317 (25.96) 0 317 (11.27)  1 (0.06) Missing  10 26  79 0 Age at diagnosis (Year) ≤65 514 (41.75) 926 (57.20) 1469 (50.77)  N/A  >65 717 (58.25) 693 (42.80) 1424 (49.23)  N/A T-stage T0  2 (0.16)  7 (0.44)  9 (0.32) N/A T1 147 (12.07) 933 (58.24) 1080 (38.30)  N/A T2 242 (19.87) 662 (41.32) 904 (32.06) N/A T3 724 (59.44) 0 724 (25.67) N/A T4 103 (8.46)  0 103 (3.65)  N/A TX  13 17  30 N/A N-stage N0 222 (70.03)  302 (100.00) 524 (84.65) N/A N1  95 (29.97) 0  95 (15.35) N/A NX 914 1317   2231  N/A M-stage M0 589 (68.25)  655 (100.00) 1244 (81.95)  N/A M1 274 (31.75) 0 274 (18.05) N/A MX 368 964  1332  N/A Gleason (biopsy) ≤4    9 (0.83) 98 (6.32) 107 (4.06)  N/A 5 43 (3.96) 247 (15.93) 290 (10.99) N/A 6 153 (14.08) 832 (53.64) 985 (37.34) N/A 7 414 (38.09) 374 (24.11) 788 (29.87) N/A 8 258 (23.74) 0 258 (9.78)  N/A 9 185 (17.02) 0 185 (7.01)  N/A 10  25 (2.30) 0 25 N/A Missing 144 68  255  N/A 43 patients can not be classifed as aggressive or localized cases because of the missing phenotypes

TABLE 2 Association of SNPs at five chromosomal regions with prostate cancer diagnosis Allelic tests Chromosomal Alternative Associated Frequency SNP id region Position^(a) alleles allele^(b) Cases Controls OR^(c) (95% CI) rs4430796 17q12 33,172,153 T, C T 0.61 0.56 1.24 (1.14-1.36) rs7501939 17q12 33,175,269 G, A G 0.66 0.62 1.22 (1.12-1.33) rs3760511 17q12 33,180,426 A, C C 0.41 0.38 1.17 (1.07-1.27) rs1859962 17q24.3 66,620,348 G, T G 0.54 0.50 1.17 (1.08-1.28) rs7214479 17q24.3 66,702,544 C, T T 0.50 0.48 1.08 (0.99-1.18) rs6501455 17q24.3 66,713,406 A, G A 0.56 0.54 1.09 (1.00-1.19) rs983085 17q24.3 66,723,656 A, G A 0.57 0.55 1.07 (0.98-1.16) rs6983561 8q24 (Region 2) 128,176,062 A, C C 0.06 0.03 1.65 (1.33-2.05) rs16901979 8q24 (Region 2) 128,194,098 C, A A 0.06 0.03 1.65 (1.33-2.05) rs6983267 8q24 (Region 3) 128,482,487 G, T G 0.56 0.51 1.22 (1.12-1.33) rs7000448 8q24 (Region 3) 128,510,352 C, T T 0.43 0.40 1.15 (1.06-1.25) rs1447295 8q24 (Region 1) 128,554,220 C, A A 0.17 0.14 1.21 (1.07-1.36) rs4242382 8q24 (Region 1) 128,586,755 G, A A 0.16 0.14 1.24 (1.10-1.39) rs7017300 8q24 (Region 1) 128,594,450 T, C C 0.20 0.18 1.15 (1.03-1.28) rs10090154 8q24 (Region 1) 128,601,319 C, T T 0.16 0.13 1.26 (1.11-1.42) rs7837688 8q24 (Reqion 1) 128,608,542 G, T T 0.15 0.13 1.17 (1.04-1.13) Best-fitting genetic model^(d) Allelic tests Genotype^(e) SNP id P^(c) Model Reference Associated OR 95% CI P^(f) rs4430796 6.0E−07 Recessive CC or TC TT 1.40 1.23-1.59 2.68E−07 rs7501939 9.0E−06 Recessive AA or GA GG 1.33 1.17-1.50 5.54E−06 rs3760511 5.0E−04 Recessive AA or CA CC 1.42 1.20-1.68 4.47E−05 rs1859962 2.1E−04 Recessive GT or TT GG 1.28 1.12-1.46 3.54E−04 rs7214479 0.07 Recessive CC or CT TT 1.15 1.00-1.32 0.06 rs6501455 0.05 Recessive AG or GG AA 1.13 0.99-1.29 0.06 rs983085 0.13 Recessive GA or GG AA 1.11 0.97-1.26 0.12 rs6983561 4.2E−06 Dominant AA CA or CC 1.60 1.28-2.00 2.14E−05 rs16901979 4.3E−06 Dominant CC AA or CA 1.60 1.28-2.01 2.14E−05 rs6983267 3.9E−06 Dominant TT GT or GG 1.38 1.19-1.59 1.74E−05 rs7000448 1.4E−03 Dominant CC CT or TT 1.18 1.04-1.33 1.21E−02 rs1447295 1.6E−03 Dominant CC CA or AA 1.26 1.10-1.44 8.27E−04 rs4242382 5.3E−04 Dominant GG AG or AA 1.29 1.12-1.47 2.53E−04 rs7017300 0.01 Dominant CC CT or TT 1.20 1.05-1.36 6.20E−03 rs10090154 2.0E−04 Dominant CC CT or TT 1.31 1.14-1.50 1.03E−04 rs7837688 9.6E−03 Dominant GG GT or TT 1.21 1.06-1.39 5.87E−03 ^(a)Position is based on NCBI Build 35. ^(b)Alleles reported to be associated with prostate cancer in previously published studies (Ref 5-9, 11). ^(c)Allelic odds ratio is based on the multiplicative model ^(d)The best-fitting model for each SNP was determined after testing associations of a series of genetic models, including dominant and recessive models, with prostate cancer in the current study ^(e)Reference and prostate cancer associated genotypes for each SNP were defined based on the best-fitting genetic model ^(f)P-value is based on likelihood ratio test (1-df tests, ajusted for age and geographic region, two-sided)

TABLE 3 Adjusted OR and PAR for representative SNPs at five chromosomal regions and family history Chromosomal Alternative Frequency of associated factors^(b) Variables/SNPs^(a) Region alleles Reference Cases Controls b^(c) OR (95% CI) P^(d) PAR (%) Age 0.01 1.01 (1.00-1.02) 0.02 Geographic −0.77 0.46 (0.39-0.54) <0.0001 Family history No Yes 0.19 0.09 0.80 2.22 (1.83-2.68) 1.15E−17 9.89 rs4430796 17q12 T, C CC/TC TT 0.38 0.30 0.32 1.38 (1.21-1.57) 1.62E−06 10.23 rs1859962 17q24.3 G, T GT/TT GG 0.30 0.25 0.24 1.28 (1.11-1.47) 5.49E−04 6.54 rs16901979 8q24 (Region 2) C, A CC AA/CA 0.10 0.07 0.42 1.53 (1.22-1.92) 1.83E−04 3.58 rs6983267 8q24 (Region 3) G, T TT GT/GG 0.82 0.77 0.32 1.37 (1.18-1.59) 3.44E−05 22.17 rs1447295 8q24 (Region 1) C, A CC CA/AA 0.31 0.26 0.19 1.22 (1.06-1.40) 5.31E−03 5.41 Joint-all five SNPs 40.45 Joint-all five SNPs and family history 46.34 ^(a)Family history and five SNPs are included in the multivariate logistic regression model adjusting for age and geographic ^(b)For SNPs, the reference and prostate cancer associated genotypes at each SNP are determined based on the best-fitting model after associations of a series of genetic models with prostate cancer in the current study ^(c)Regression coefficient ^(d)Based on likelihood ratio test

TABLE 4 Cumulative effect of associated factors on prostate cancer # of associated # (%) of subjects Cases vs. Controls factors Controls Cases b^(e) OR 95% CI P^(f) P^(g) Number of prostate cancer associated genotypes at five SNPs^(a) Age — — 0.01 1.01 1.00-1.02 0.02 Geographic region — — −0.76 0.46 0.40-0.55 <0.0001 Family history — — 0.8 2.22 1.83-2.68 7.73E−18 0 associated genotype^(c) 173 (10.09) 162 (5.64) NA 1.00 — — 1 associated genotype^(c) 631 (36.79)  883 (30.77) 0.41 1.50 1.18-1.92 9.46E−04 2 associated genotypes^(c) 618 (36.03) 1123 (39.13) 0.67 1.96 1.54-2.49 4.19E−08 3 associated genotypes^(c) 255 (14.87)  548 (19.09) 0.79 2.21 1.70-2.89 4.33E−09 ≥4 associated genotypes^(c) 38 (2.22) 154 (5.37) 1.5 4.47 2.93-6.80 1.20E−13 6.75E−27 Number of prostate cancer associated factors (genotypes at five SNPs and family history) ^(b) Age — — 0.01 1.01 1.00-1.02 0.02 Geographic region — — −0.75 0.47 0.40-0.55 <0.0001 0 associated factor^(d) 174 (10.07) 144 (4.98) NA 1.00 — — 1 associated factor^(d) 581 (33.62)  778 (26.89) 0.48 1.62 1.27-2.08 1.27E−04 2 associated factors^(d) 622 (36.00) 1053 (36.40) 0.73 2.07 1.62-2.64 5.86E−09 3 associated factors^(d) 286 (16.55)  642 (22.19) 0.99 2.71 2.08-3.53 9.54E−14 4 associated factors^(d) 60 (3.47) 236 (8.16) 1.56 4.76 3.31-6.84 9.17E−19 ≥5 associated factors^(d)  5 (0.29)  40 (1.38) 2.24 9.46  3.62-24.72 1.29E−08 4.78E−28 ^(a)Testing for cumulative effect of five SNPs (rs4430796, rs1859962, rs16901979, rs6983267, and rs1447295) adjusting for age, geographic region, and family history ^(b) Testing for cumulative effect of the five SNPs plus family history adjusting for age and geographic region ^(c)Number of prostate cancer associated genotypes at the five SNPs ^(d)Number of prostate cancer associated factors (the five SNPs plus family history) ^(e)Regression coefficient ^(f)P-value is based on likelihood-ratio test, two-sided ^(g)P-value is based on Armitage trend test

TABLE 5 Association of five SNPs with clinical characteristics^(a) rs4430796 (17q12) rs1859962 (17q24.3) rs16901979 (8q24) Clinical Reference^(a) Associated^(a) Reference^(a) Associated^(a) Reference^(a) characteristics CC/TC TT GT/TT GG CC           Number (%) of subjects at each genotype Aggressiveness Localized 1021 (63.50)  587 (36.50) 1113 (69.39)  491 (30.61) 1446 (90.21)  Aggressive 748 (61.46) 469 (38.54) 860 (70.78) 355 (29.22) 1077 (88.71)  P^(b) 0.27 0.42 Gleason score (biopsy) ≤4  69 (65.71)  36 (34.29)  69 (65.71)  36 (34.29)  96 (91.43) 5 182 (62.98) 107 (37.02) 200 (69.44)  88 (30.56) 256 (89.51) 6 619 (63.29) 359 (36.71) 675 (69.16) 301 (30.84) 881 (90.27) 7 497 (63.64) 284 (36.36) 554 (71.21) 224 (28.79) 701 (90.10) 8 152 (59.61) 103 (40.39) 184 (72.16)  71 (27.84) 215 (84.31) 9 106 (57.61)  78 (42.39) 126 (68.48)  58 (31.52) 165 (89.67) 10  13 (52.00)  12 (48.00)  19 (76.00)  6 (24.00)  24 (96.00) P^(c) 0.08 0.30 Family history in first degree relatives No 1466 (63.08)  858 (36.92) 1623 (70.02)  695 (29.98) 2066 (89.17)  Yes 331 (60.85) 213 (39.15) 380 (69.85) 164 (30.15) 491 (90.42) P^(d) 0.33 0.94 Mean or median at each genotype PSA levels at diagnosis (ng/ml) Median 12.00 13.00  13.00 11.90  12.00 P^(e) 0.83 0.66 Age at diagnosis (Year) Mean 65.86 65.72  65.91 65.55  65.79 P^(f) 0.63 0.22 rs16901979 (8q24) rs6983267 (8q24) rs1447295 (8q24) Clinical Associated^(a) Reference^(a) Associated^(a) Reference^(a) Associated^(a) characteristics AA/CA TT GT/GG CC CA/AA          Number (%) of subjects at each genotype Aggressiveness Localized 157 (9.79)  294 (18.41) 1303 (81.59)  1130 (70.49)  473 (29.51) Aggressive 137 (11.29)  243 (20.03) 970 (79.97) 838 (69.03) 376 (30.97) P^(b) 0.20 0.28 0.40 Gleason score (biopsy) ≤4 9 (8.57)  22 (20.95)  83 (79.05)  80 (76.19)  25 (23.81) 5 30 (10.49)  61 (21.25) 226 (78.75) 198 (68.99)  89 (31.01) 6 95 (9.73)  170 (17.49) 802 (82.51) 697 (71.34) 280 (28.66) 7 77 (9.90)  161 (20.75) 615 (79.25) 536 (69.07) 240 (30.93) 8 40 (15.69)  47 (18.50) 207 (81.50) 179 (70.20)  76 (29.80) 9 19 (10.33)  32 (17.39) 152 (82.61) 128 (69.57)  56 (30.43) 10 1 (4.00)  8 (32.00)  17 (68.00)  18 (72.00)  7 (28.00) P^(c) 0.28 0.97 0.43 Family history in first degree relatives No 251 (10.83)  451 (19.50) 1862 (80.50)  1628 (70.26)  689 (29.74) Yes 52 (9.58)   94 (17.41) 446 (82.59) 370 (68.14) 173 (31.86) P^(d) 0.39 0.27 0.33 Mean or median at each genotype PSA levels at diagnosis (ng/ml) Median 14.50  12.00 12.00  12.00 13.00  P^(e) 0.16 0.17 0.07 Age at diagnosis (Year) Mean 65.84  65.76 65.80  65.85 65.67  P^(f) 0.91 0.90 0.54 ^(a)Reference or prostate cancer associated genotypes are determined based on the the best-fitting model at each SNP in the current study ^(b,d)Pearson Chi-square test, two-sided ^(c)Armitage trend test, two-sided ^(e)Wilcoxon rank sum test, two-sided ^(f)Two-sample t test, two-sided

TABLE 6 Cummulative effect of risk factors on prostate cancer risk in JHHAA # of risk factors Number of risk genotypes at # (%) of subjects Cases vs. Controls five SNPs ^(a) Controls Cases OR 95% CI P ^(b) 0 or 1  91 (25.85)  62 (16.71) 1 2 142 (40.34) 150 (40.43) 1.55 1.04-2.30 0.03 3  3 (25.85) 121 (32.61) 1.95 1.28-2.98 0.002 ≥4 28 (7.95)  38 (10.24) 1.99 1.11-3.58 0.02 ^(a) Assuming the best-fit model at each SNP ^(b) P-value is based on likelihood-ratio test, two-sided

TABLE 7 Stronger cumulative effect among prostate cancer with early age of onset Cases % Cases Controls % Controls OR 95% L 95% U All cases 0 risk factor 119 0.04 160 0.09 1 1 risk factor 755 0.27 573 0.34 1.77 1.35 2.3 2 risk factors 1040 0.37 619 0.36 2.28 1.75 2.96 3 risk factors 640 0.23 285 0.17 2.99 2.26 3.95 4 risk factors 234 0.08 59 0.03 5.3 3.64 7.72 >=5 risk factors 39 0.01 5 0 9.75 3.7 25.67 <65 years 0 risk factor 53 0.04 70 0.1 1 1 risk factor 351 0.27 225 0.32 2.04 1.37 3.04 2 risk factors 466 0.35 262 0.38 2.34 1.58 3.47 3 risk factors 310 0.24 113 0.16 3.53 2.31 5.39 4 risk factors 118 0.09 25 0.04 6.29 3.58 11.08 >=5 risk factors 21 0.02 1 0 25.94 3.26 200.02 65-69 years 0 risk factor 24 0.04 21 0.07 1 1 risk factor 161 0.26 113 0.36 1.21 0.6 2.45 2 risk factors 231 0.38 101 0.32 1.95 0.97 3.94 3 risk factors 143 0.23 67 0.21 1.8 0.87 3.73 4 risk factors 43 0.07 13 0.04 2.87 1.13 7.27 >=5 risk factors 10 0.02 1 0 8.27 0.9 75.66 >69 years 0 risk factor 42 0.05 69 0.1 1 1 risk factor 243 0.27 235 0.34 1.66 1.08 2.54 2 risk factors 343 0.38 256 0.37 2.16 1.42 3.29 3 risk factors 187 0.21 105 0.15 2.88 1.83 4.55 4 risk factors 73 0.08 21 0.03 5.64 3.03 10.49 >=5 risk factors 8 0.01 3 0 4.51 1.13 17.98

TABLE 8 11 SNPs with risk alleles in chromosome region 17q12; boundary from 33,163,028 to 33,189,279 CHR SNP POSITION RISK ALLELE 17 rs1016990 33163028 G 17 rs3744763 33164998 A 17 rs2005705 33170413 G 17 rs757210 33170628 C 17 rs4430796 33172153 A 17 rs4239217 33173100 A 17 rs7501939 33175269 C 17 rs3760511 33180426 G 17 rs17626423 33182480 C 17 rs17626459 33185868 A 17 rs7213769 33189279 G All the positions are based on Build 35 and the SNPs are based on Hapmap SNP release 21. Additional markers are also claimed if they are in strong linkage disequilibrium, as defined by D′ > 0.8 and/or r2 > 0.2, with any marker listed in this table

TABLE 9 174 SNPs with risk alleles from chromosome region 17q24.3; boundary from 66,616,533 to 66,754,527 CHR SNP POSITION RISK ALLELE 17 rs7222314 66616533 A 17 rs16976411 66616945 T 17 rs991528 66617179 A 17 rs17765344 66618469 A 17 rs8071558 66619268 C 17 rs8072254 66619411 A 17 rs984434 66619722 T 17 rs1859962 66620348 G 17 rs11650165 66621213 C 17 rs991429 66621368 G 17 rs4793528 66622368 A 17 rs9674957 66622693 G 17 rs8077906 66623828 G 17 rs8066875 66625172 A 17 rs9889335 66626741 T 17 rs4328484 66627825 G 17 rs8068266 66628530 A 17 rs12947919 66629682 T 17 rs4793529 66630231 T 17 rs7217652 66631076 T 17 rs6501437 66631567 G 17 rs6501438 66631755 A 17 rs8079315 66632450 C 17 rs2367256 66632881 A 17 rs2190697 66632936 A 17 rs4366746 66633226 G 17 rs4366747 66633238 G 17 rs2159034 66633350 C 17 rs1013999 66633530 C 17 rs4793530 66636858 C 17 rs11654749 66637201 G 17 rs11653132 66641427 G 17 rs4300694 66642431 T 17 rs8076830 66643504 T 17 rs9900242 66647226 G 17 rs9908442 66649543 G 17 rs4793334 66649577 A 17 rs2058083 66649998 C 17 rs2058084 66650612 C 17 rs2058085 66650642 A 17 rs1468481 66651574 C 17 rs9915190 66654223 C 17 rs8065751 66654373 T 17 rs8080251 66654402 A 17 rs17178083 66654469 G 17 rs2041114 66656212 A 17 rs723338 66657008 A 17 rs2041115 66658022 A 17 rs8064263 66658425 A 17 rs9897865 66658671 C 17 rs11656242 66659117 A 17 rs9897358 66659137 G 17 rs11651123 66659186 G 17 rs11657298 66659231 T 17 rs11651469 66660114 T 17 rs11651501 66660153 T 17 rs719615 66661505 G 17 rs7219299 66662350 G 17 rs9916274 66663128 C 17 rs7209594 66663375 T 17 rs1558119 66663567 C 17 rs12150098 66667429 T 17 rs17824720 66668449 T 17 rs9747823 66669172 C 17 rs9910829 66671172 A 17 rs7220274 66671362 A 17 rs17224833 66672070 A 17 rs2108534 66672495 A 17 rs2108535 66672740 T 17 rs8182284 66672986 T 17 rs8182286 66673149 T 17 rs4793533 66676066 T 17 rs8069925 66676457 A 17 rs8068189 66676490 G 17 rs9901508 66676794 A 17 rs9907418 66676814 T 17 rs2367263 66677883 G 17 rs1859964 66678166 T 17 rs1859965 66678690 C 17 rs6501446 66679653 T 17 rs4793534 66679888 C 17 rs4239156 66679976 T 17 rs4793335 66680286 G 17 rs2108536 66681371 G 17 rs7216882 66682565 A 17 rs2097984 66682821 T 17 rs11654068 66684131 C 17 rs8079962 66684297 A 17 rs6501447 66684693 T 17 rs7206969 66684744 G 17 rs2886914 66685408 C 17 rs9909797 66686777 G 17 rs8076811 66687002 A 17 rs1859966 66687338 C 17 rs17178251 66688474 C 17 rs7211425 66691073 G 17 rs17765644 66691087 T 17 rs9913988 66691653 T 17 rs11871129 66692057 C 17 rs758106 66692598 T 17 rs740408 66692691 A 17 rs17224938 66694338 T 17 rs17824822 66695549 T 17 rs4570900 66697961 G 17 rs1011729 66699182 C 17 rs1011730 66699340 A 17 rs16976453 66700323 A 17 rs4611499 66700364 T 17 rs6501448 66701508 G 17 rs7208398 66702451 T 17 rs7214479 66702544 T 17 rs1008348 66702911 G 17 rs2367265 66702962 C 17 rs6501449 66704440 C 17 rs6501451 66704726 T 17 rs6501452 66704882 G 17 rs8079118 66705375 G 17 rs11870732 66706836 A 17 rs17178370 66707136 T 17 rs17225050 66709084 T 17 rs7225025 66709269 A 17 rs2215050 66709704 A 17 rs17178377 66709728 G 17 rs11655744 66710651 T 17 rs2367266 66711582 G 17 rs1107305 66712238 G 17 rs6501454 66713352 T 17 rs6501455 66713406 A 17 rs7209505 66715259 A 17 rs7209069 66715391 T 17 rs13342783 66717627 G 17 rs8067671 66718759 C 17 rs2190463 66719063 T 17 rs2190456 66722961 C 17 rs983084 66723461 G 17 rs983085 66723656 A 17 rs6501459 66725050 C 17 rs4793538 66727523 C 17 rs2158905 66727636 C 17 rs2190457 66728004 T 17 rs11655567 66728282 T 17 rs7225458 66729941 A 17 rs10401004 66730351 A 17 rs7215164 66731916 G 17 rs917278 66733520 C 17 rs1978203 66734264 T 17 rs1978204 66734540 G 17 rs737956 66735463 G 17 rs737957 66735504 A 17 rs8075481 66735791 C 17 rs8080004 66735894 T 17 rs7224058 66737374 G 17 rs7215307 66737962 T 17 rs4793541 66739190 T 17 rs7221080 66741567 G 17 rs8064388 66742612 G 17 rs8076167 66744678 T 17 rs8067695 66745906 T 17 rs9898561 66746448 G 17 rs9906756 66747639 A 17 rs17178530 66747707 G 17 rs17765886 66747800 C 17 rs12946942 66748593 T 17 rs16976482 66749123 C 17 rs9302933 66750402 G 17 rs9914509 66750867 G 17 rs9895657 66751026 C 17 rs12941471 66751530 A 17 rs9896822 66751706 A 17 rs8070461 66752467 G 17 rs2214946 66753475 T 17 rs9909596 66753588 G 17 rs16976490 66753642 T 17 rs9891216 66754527 C All the positions are based on Build 35 and the SNPs are based on Hapmap SNP release 21. Additional markers are also to be included if they are in strong linkage disequilibrium, as defined by D′ > 0.8 and/or r2 > 0.2, with any marker listed in this table.

TABLE 10 116 SNPs with risk alleles from chromosome region 8q24 (region 1); boundary from 128,536,936 to 128,617,860 CHR SNP POSITION RISK ALLELE 8 rs7017671 128536936 C 8 rs10099905 128537116 A 8 rs10956372 128539438 T 8 rs7830412 128540223 A 8 rs7387447 128540858 C 8 rs10094871 128541151 A 8 rs1447293 128541502 C 8 rs921146 128544367 G 8 rs7825118 128544999 A 8 rs2121630 128547342 T 8 rs3999775 128548719 T 8 rs4871798 128549145 T 8 rs10089310 128550166 T 8 rs7819102 128550531 C 8 rs4871799 128551824 G 8 rs6981424 128552278 G 8 rs6470519 128553405 A 8 rs7818556 128553581 G 8 rs1447295 128554220 A 8 rs10109700 128555146 A 8 rs9297758 128555770 G 8 rs13363309 128556704 G 8 rs13259396 128557893 A 8 rs13260378 128557932 G 8 rs7826179 128558381 T 8 rs10956373 128559758 C 8 rs7836840 128560974 T 8 rs7831028 128561211 C 8 rs1992833 128561526 T 8 rs2290033 128562256 G 8 rs9643225 128563573 T 8 rs9643226 128563663 C 8 rs11775749 128563848 A 8 rs11994384 128564509 G 8 rs1447296 128564541 T 8 rs16902168 128564790 T 8 rs9643227 128565278 C 8 rs16902169 128565688 A 8 rs13253127 128565773 T 8 rs6985504 128565958 A 8 rs13258548 128566029 A 8 rs13258812 128566049 C 8 rs16902172 128567224 G 8 rs1447297 128567804 C 8 rs7831150 128568620 G 8 rs723555 128569281 G 8 rs10808558 128570332 A 8 rs10103005 128571003 G 8 rs7820229 128571765 C 8 rs16902173 128573181 A 8 rs17766217 128573679 T 8 rs4871806 128574318 C 8 rs12155672 128576206 A 8 rs12156128 128576373 C 8 rs1562434 128576501 C 8 rs1562433 128576632 G 8 rs1562432 128576784 T 8 rs1562431 128576833 C 8 rs12056473 128577104 A 8 rs12056788 128577254 G 8 rs4599773 128579606 C 8 rs4078240 128580745 A 8 rs6981321 128582487 C 8 rs4871808 128582727 C 8 rs7832031 128586134 A 8 rs4242382 128586755 A 8 rs4242383 128586942 A 8 rs4314621 128587197 G 8 rs4242384 128587736 C 8 rs7018386 128589139 C 8 rs7812429 128589355 A 8 rs7812894 128589661 A 8 rs4871026 128589959 C 8 rs4871027 128590689 G 8 rs10099413 128591245 T 8 rs7814837 128591384 T 8 rs10088308 128592096 C 8 rs9297760 128592354 A 8 rs7007540 128592822 A 8 rs7824868 128593596 T 8 rs7017300 128594450 C 8 rs12547874 128594814 A 8 rs6470526 128595073 G 8 rs7004374 128595167 T 8 rs7005343 128595760 A 8 rs9693113 128596612 C 8 rs4871809 128596737 T 8 rs7461151 128596912 A 8 rs6470527 128597013 A 8 rs6470528 128597549 A 8 rs4582524 128597617 G 8 rs4498506 128598215 A 8 rs4297007 128598298 G 8 rs4242385 128598411 G 8 rs11992171 128599115 C 8 rs13255059 128599798 A 8 rs13265719 128600210 T 8 rs11986220 128600871 A 8 rs11988857 128601055 G 8 rs10090154 128601319 T 8 rs10103849 128601549 G 8 rs7824776 128602624 C 8 rs7843031 128602655 T 8 rs4531012 128603543 G 8 rs9656967 128603769 T 8 rs9656816 128603836 G 8 rs12548153 128603874 T 8 rs12542685 128606765 A 8 rs7814251 128607399 C 8 rs9694093 128608330 G 8 rs7837688 128608542 T 8 rs7825823 128611099 C 8 rs6991990 128614565 C 8 rs11988207 128616342 A 8 rs12386846 128617631 T 8 rs13258742 128617860 G All the positions are based on Build 35 and the SNPs are based on Hapmap SNP release 21. Additional markers are also to be included if they are in strong linkage disequilibrium, as defined by D′ > 0.8 and/or r2 > 0.2, with any marker listed in this table.

TABLE 11 112 SNPs with risk alleles from chromosome region 8q24 (region 2); boundary from 128,469,358 to 128,535,996 CHR SNP POSITION RISK ALLELE 8 rs3940781 128145397 A 8 rs16901935 128145727 A 8 rs12542102 128146703 T 8 rs11988135 128148591 T 8 rs2392727 128148778 A 8 rs2392728 128148825 G 8 rs2392729 128148848 T 8 rs1014656 128148865 T 8 rs2392730 128148876 A 8 rs2392731 128148922 T 8 rs2392732 128149116 A 8 rs2893603 128149139 T 8 rs9656965 128149430 C 8 rs17831626 128149605 T 8 rs16901938 128149682 A 8 rs7824679 128150292 A 8 rs7824923 128150320 T 8 rs7843300 128150413 C 8 rs7824957 128150442 A 8 rs7825414 128150703 G 8 rs13282364 128151591 A 8 rs13363429 128152031 A 8 rs6993569 128153279 G 8 rs6994316 128153721 G 8 rs12550334 128154106 G 8 rs11998124 128154662 C 8 rs6999589 128154828 A 8 rs11998248 128154937 G 8 rs1902431 128156258 T 8 rs6470494 128157086 T 8 rs1016342 128161637 T 8 rs1551511 128161996 G 8 rs1031588 128162459 A 8 rs1016343 128162479 T 8 rs1551510 128162660 T 8 rs4871008 128162723 C 8 rs6981122 128163642 C 8 rs13252298 128164338 A 8 rs7841060 128165659 G 8 rs7007694 128168348 C 8 rs4571699 128168451 A 8 rs1840709 128168637 G 8 rs11993508 128169258 C 8 rs17832021 128169358 A 8 rs3857883 128169788 G 8 rs1456316 128170030 A 8 rs16901946 128170107 G 8 rs9656813 128170146 A 8 rs9656814 128170159 C 8 rs10505484 128170494 G 8 rs7844454 128171683 T 8 rs12682421 128172333 G 8 rs1456315 128173119 T 8 rs13254738 128173525 C 8 rs12682344 128175966 G 8 rs6983561 128176062 C 8 rs16901948 128176283 A 8 rs1869931 128176318 A 8 rs16901949 128176335 C 8 rs16901950 128176425 A 8 rs16901952 128176452 C 8 rs16901953 128177411 C 8 rs7010450 128177861 G 8 rs6990420 128177907 T 8 rs17832285 128178175 A 8 rs7825340 128178311 G 8 rs12544977 128178469 A 8 rs16901959 128178712 G 8 rs7826337 128178756 G 8 rs7826388 128178958 T 8 rs7830341 128179112 A 8 rs16901966 128179434 G 8 rs16901967 128179459 G 8 rs7000910 128179787 A 8 rs7001069 128179828 G 8 rs17765137 128179996 A 8 rs11781162 128180078 A 8 rs11774449 128180214 T 8 rs7006409 128180611 G 8 rs6988257 128180638 C 8 rs16901969 128181279 C 8 rs16901970 128181897 G 8 rs10453084 128181961 A 8 rs6987723 128182041 A 8 rs6987640 128182210 T 8 rs3956788 128183074 C 8 rs7824451 128183643 G 8 rs7824785 128183892 T 8 rs13268116 128183978 G 8 rs6470498 128184902 G 8 rs1011829 128184925 A 8 rs1456306 128185682 G 8 rs7844219 128187997 G 8 rs1378897 128191841 T 8 rs1551512 128193308 G 8 rs16901979 128194098 A 8 rs10505483 128194377 T 8 rs7817677 128194686 G 8 rs10505482 128195031 A 8 rs1456305 128196434 A 8 rs17184796 128197641 T 8 rs16901983 128198224 T 8 rs6989838 128198554 C 8 rs7013255 128199669 G 8 rs16901984 128200143 C 8 rs10098156 128200973 G 8 rs6995291 128201506 A 8 rs16901985 128203159 A 8 rs7824364 128204547 C 8 rs7816535 128206850 A 8 rs16901988 128207742 C 8 rs6470500 128215780 A All the positions are based on Build 35 and the SNPs are based on Hapmap SNP release 21. Additional markers are also to be included if they are in strong linkage disequilibrium, as defined by D′ > 0.8 and/or r2 > 0.2, with any marker listed in this table.

TABLE 12 70 SNPs with risk alleles from chromosome region 8q24 (Region 3); boundary from 128,469,358 to 128,535,996 CHR SNP POSITION RISK ALLELE 8 rs7820981 128469358 C 8 rs1562871 128470954 T 8 rs12549845 128472089 G 8 rs10441525 128472135 C 8 rs7844673 128472696 G 8 rs10956365 128473069 A 8 rs16902147 128474254 C 8 rs16902148 128476181 A 8 rs16902149 128476287 C 8 rs3847136 128476372 A 8 rs10505477 128476625 A 8 rs12334317 128477246 C 8 rs10505476 128477298 T 8 rs11985829 128478414 T 8 rs10808555 128478693 G 8 rs10505475 128480639 G 8 rs17467139 128481192 G 8 rs10808556 128482329 C 8 rs6983267 128482487 G 8 rs3847137 128483680 C 8 rs7013278 128484074 T 8 rs10505474 128486686 T 8 rs10505473 128487118 T 8 rs11986916 128488689 C 8 rs10098876 128489098 G 8 rs2060776 128489299 G 8 rs13248944 128489740 C 8 rs4871788 128490967 G 8 rs7837328 128492309 A 8 rs7837626 128492523 A 8 rs7837644 128492580 A 8 rs10956368 128492832 T 8 rs10956369 128492999 T 8 rs7014346 128493974 A 8 rs871135 128495575 G 8 rs6985419 128498903 T 8 rs7842552 128500876 G 8 rs12375310 128501388 A 8 rs7005829 128504126 T 8 rs1447294 128506868 T 8 rs9297756 128509349 A 8 rs6995633 128509833 A 8 rs6999789 128510043 C 8 rs6999921 128510110 G 8 rs7000448 128510352 T 8 rs6982665 128510403 C 8 rs7357486 128510805 T 8 rs7357368 128512569 T 8 rs7829370 128515566 T 8 rs12334463 128518887 C 8 rs13280578 128519269 A 8 rs6470512 128519904 T 8 rs7007536 128520151 G 8 rs10090421 128522947 G 8 rs12334695 128523110 C 8 rs6981397 128524836 A 8 rs7831606 128524876 A 8 rs10101741 128525201 A 8 rs7012462 128526872 T 8 rs10109622 128527333 T 8 rs10109723 128527420 G 8 rs6996874 128527491 G 8 rs4871791 128527826 C 8 rs13282506 128528307 G 8 rs6470517 128529586 A 8 rs7841228 128530060 G 8 rs10094059 128530789 G 8 rs11781420 128534524 A 8 rs9643221 128534669 G 8 rs7841264 128535996 C All the positions are based on Build 35 and the SNPs are based on Hapmap SNP release 21. Additional markers are also to be included if they are in strong linkage disequilibrium, as defined by D′ > 0.8 and/or r2 > 0.2, with any marker listed in this table.

TABLE 13 WELL TERM SNP_ID 2nd-PCRP 1st-PCRP UEP_SEQ W1 iPLEX rs4645959 ACGTTGGATGTCGTCG ACGTTGGATGTGCCC CGTAGTCGAGGTC CAGTAGAAATACGG CTCAACGTTAGCTTC ATAG W1 iPLEX rs1668875 ACGTTGGATGAGTCAT ACGTTGGATGTGCCC TACTCAGCATTCC CCTGAGTACTCAGC AGATATGAGAGTGAG CCAAAA W1 iPLEX rs12334695 ACGTTGGATGTGTGTG ACGTTGGATGCAGCA CCACTCTCTCTAT TGCACATGTGCTTG GAGCTCCATGAAAAG TCCCCTC W1 iPLEX rs7824074 ACGTTGGATGTTCATC ACGTTGGATGAAGAG AGTTCCTGTTCAC CACACTCCCATCTC GAAGACTGGGAAAGG AACCAAG W1 iPLEX rs10086908 ACGTTGGATGTTAGAT ACGTTGGATGGGAAA atagCACCTCAAA GCCCCTTCTGTGTG TTACACTTCATGATG CTTCCCCT W1 iPLEX rs6983267 ACGTTGGATGTCATCG ACGTTGGATGCTCCC ttttAGCTCAGCA TCCTTTGAGCTCAG TCCCCCACATAAAAT GATGAAAG W1 iPLEX rs7837688 ACGTTGGATGTGACGT ACGTTGGATGTTCAC ccCAACATAGACC GTCAACATAGACCC AGCCTCCCTCATTAC CAATTGTAC W1 iPLEX rs16901979 ACGTTGGATGAGTTCA ACGTTGGATGGTTGT CTCAAAAATACCA GTTCACTTTCTTCC GGAGCAGTGTTAATG TTTGCCAGA W1 iPLEX rs10505473 ACGTTGGATGACGTTG ACGTTGGATGTCAGG ATGGTTGAAATGG ACTCCTTAGAATGG TTCGTAACCTTGGTG TAGTATTCCA W1 iPLEX rs7841193 ACGTTGGATGTCCTGC ACGTTGGATGGGATA ctTAGGACAATAC CTCTTTTCCTTCAC TGGGATAGGCTTGAG CTCACCTACAT W1 iPLEX rs12375310 ACGTTGGATGCTGCTA ACGTTGGATGGCTGG TCCACTGATTATT TGAAACCACTGTCC GAGATTAAAACAAAC TTTTTGTGTTT W1 iPLEX rs7017300 ACGTTGGATGGACCAT ACGTTGGATGAAATC ggtgTCCCTTTGT GAACAATGAGATTCG ACTGCAACTGCCCTG ATGATGCCTAGA W1 iPLEX rs6470572 ACGTTGGATGACTCAG ACGTTGGATGGGCCA ccatGGACAGGCA TCACTCCAGGGACA GACTTTGAATCTTAC CCAGAAGAGATG W1 iPLEX rs6981122 ACGTTGGATGGGTCCT ACGTTGGATGAGTTA aactACCTCCTAA TCATCCCATTCTTG ACAGCAGCCGATATG AGAACCTACTATT W2 iPLEX rs4242382 ACGTTGGATGCAGGGA ACGTTGGATGGTGTT CCCTCTAGTTATC ACATTTTGTCCCTC CCTAGGTTCTCTGTG TTCCC W2 iPLEX rs1447295 ACGTTGGATGCTACCC ACGTTGGATGATTGA tTTGCTTTTTTTC CCACCAGCATTTTT GGAAGTGCCATTGGG CATAGCAC W2 iPLEX rs1467191 ACGTTGGATGTTGGGC ACGTTGGATGGGATT ACCCCAACCTTAG AACCCCAACCTTAG AAACATGTGGTGCTG ATCTTCTTTC W2 iPLEX rs622556 ACGTTGGATGACCCAT ACGTTGGATGCACAT tcGTTGAATTATC ACTCAGCCTTTACC GTTTTCTTAGGATAG ATCAACAGCTT 

1-19. (canceled)
 20. A method of measuring a prostate specific antigen (PSA) level in a subject, comprising measuring the PSA level in a sample obtained from the subject consisting essentially of: a) the T allele of single nucleotide polymorphism rs4430796; b) the G allele of single nucleotide polymorphism rs1859962; c) the A allele of single nucleotide polymorphism rs16901979; d) the G allele of single nucleotide polymorphism rs6983267; and e) the A allele of single nucleotide polymorphism rs1447295.
 21. A method of measuring a prostate specific antigen (PSA) level in a subject, comprising: (a) obtaining a nucleic acid sample from the subject; (b) detecting in the nucleic acid sample from the subject the following alleles: (1) the T allele of single nucleotide polymorphism rs4430796; (2) the G allele of single nucleotide polymorphism rsl 859962; (3) the A allele of single nucleotide polymorphism rs16901979; (4) the G allele of single nucleotide polymorphism rs6983267; and (5) the A allele of single nucleotide polymorphism rs1447295, by an amplification reaction, hybridization, restriction endonuclease digestion and/or electrophoresis, and (c) measuring the PSA level in said subject having each of the alleles (1)-(5).
 22. A method of measuring a prostate specific antigen (PSA) level in a subject, consisting essentially of: (a) obtaining a nucleic acid sample from the subject; (b) detecting in the nucleic acid sample from the subject the following alleles: (1) the T allele of single nucleotide polymorphism rs4430796; (2) the G allele of single nucleotide polymorphism rs1859962; (3) the A allele of single nucleotide polymorphism rs16901979; (4) the G allele of single nucleotide polymorphism rs6983267; and (5) the A allele of single nucleotide polymorphism rs1447295, by an amplification reaction, hybridization, restriction endonuclease digestion and/or electrophoresis, and (c) measuring the PSA level in said subject based on the subject having each of the alleles (1)-(5).
 23. A method of detecting the following alleles in a subject: (1) the T allele of single nucleotide polymorphism rs4430796; (2) the G allele of single nucleotide polymorphism rs1859962; (3) the A allele of single nucleotide polymorphism rs16901979; (4) the G allele of single nucleotide polymorphism rs6983267; and (5) the A allele of single nucleotide polymorphism rs1447295, consisting of: (a) obtaining a nucleic acid sample from the subject; and (b) detecting the alleles (1)-(5) in the nucleic acid sample by contacting the nucleic acid sample with oligonucleotides that hybridize to respective nucleotide sequences comprising each of the alleles (1)-(5) and detecting hybridization between the respective nucleotide sequences and the oligonucleotides.
 24. The method of claim 20, wherein the subject has a family history of prostate cancer.
 25. The method of claim 21, wherein the subject has a family history of prostate cancer.
 26. The method of claim 22, wherein the subject has a family history of prostate cancer.
 27. The method of claim 23, wherein the subject has a family history of prostate cancer.
 28. A method of diagnosing prostate cancer in a subject, comprising (a) obtaining a nucleic acid sample from the subject; (b) detecting in the nucleic acid sample from the subject the following alleles: (1) the T allele of single nucleotide polymorphism rs4430796; (2) the G allele of single nucleotide polymorphism rs1859962; (3) the A allele of single nucleotide polymorphism rs16901979; (4) the G allele of single nucleotide polymorphism rs6983267; and (5) the A allele of single nucleotide polymorphism rs1447295, by an amplification reaction, hybridization, restriction endonuclease digestion and/or electrophoresis; (c) measuring the PSA level in the subject having each allele (1)-(5); and (d) diagnosing the subject as having prostate cancer based on the measured PSA level of the subject and the presence of each allele (1)-(5).
 29. The method of claim 28, wherein the subject has a family history of prostate cancer.
 30. A method of identifying a subject as having an increased risk of developing prostate cancer, comprising: (a) obtaining a nucleic acid sample from the subject; (b) detecting in the nucleic acid sample from the subject the following alleles: (1) the T allele of single nucleotide polymorphism rs4430796; (2) the G allele of single nucleotide polymorphism rs1859962; (3) the A allele of single nucleotide polymorphism rs16901979; (4) the G allele of single nucleotide polymorphism rs6983267; and (5) the A allele of single nucleotide polymorphism rs1447295, by an amplification reaction, hybridization, restriction endonuclease digestion and/or electrophoresis; (c) measuring the PSA level in a subject having each allele (1)-(5); and (d) identifying the subject as having an increased risk of developing prostate cancer based on the measured PSA level of the subject and the presence of each allele (1)-(5).
 31. The method of claim 30, wherein the subject has a family history of prostate cancer. 